IADR Abstract Archives
Optimizing Scaffold Design via Machine Learning
Objectives: To investigate the effects of varying pore sizes and porosities on osteogenesis in 3D biomaterial scaffolds and apply machine learning algorithms to analyze the experimental data, identify key factors influencing osteoblast behavior, and ultimately predict the optimal pore structure that enhances osteoblast differentiation and bone regeneration.
Methods: Nine experimental groups were designed with varying pore sizes (50–100 μm, 150–200 μm, 300–400 μm) and porosities (30–50%, 50–70%, 70–90%). Osteoblast-like cells were cultured on these scaffolds for 1, 3, 7, 14, and 21 days, and key osteogenic indicators—including proliferation, survival, early markers (ALP, RUNX2, BMP2), and matrix mineralization—were measured. Machine learning techniques analyzed the data, with multiple regression exploring relationships between scaffold properties and outcomes, random forest identifying key features, and SVM/KNN predicting optimal configurations. PCA further visualized data patterns, enabling refinement of scaffold designs for enhanced osteogenesis.
Results: Experimental results showed that scaffolds with medium pore sizes (150–200 μm) and porosity (50–70%) maximized osteoblast proliferation and differentiation. Machine learning identified porosity as the key predictor of osteoblast activity, with medium porosity providing the best balance for cell attachment, migration, and nutrient exchange. Random forest and SVM/KNN models confirmed these configurations as optimal, while PCA highlighted distinct patterns supporting medium pore size and porosity for enhanced osteogenesis.
Conclusions: This study integrates experiments with machine learning to optimize 3D biomaterial scaffold design for bone regeneration. Using models like regression, random forests, SVM, KNN, and PCA, scaffolds with a pore size of 150–200 μm and porosity of 50–70% were identified as optimal for osteoblast proliferation and differentiation. This data-driven approach enhances scaffold optimization and accelerates advanced tissue engineering development.
Methods: Nine experimental groups were designed with varying pore sizes (50–100 μm, 150–200 μm, 300–400 μm) and porosities (30–50%, 50–70%, 70–90%). Osteoblast-like cells were cultured on these scaffolds for 1, 3, 7, 14, and 21 days, and key osteogenic indicators—including proliferation, survival, early markers (ALP, RUNX2, BMP2), and matrix mineralization—were measured. Machine learning techniques analyzed the data, with multiple regression exploring relationships between scaffold properties and outcomes, random forest identifying key features, and SVM/KNN predicting optimal configurations. PCA further visualized data patterns, enabling refinement of scaffold designs for enhanced osteogenesis.
Results: Experimental results showed that scaffolds with medium pore sizes (150–200 μm) and porosity (50–70%) maximized osteoblast proliferation and differentiation. Machine learning identified porosity as the key predictor of osteoblast activity, with medium porosity providing the best balance for cell attachment, migration, and nutrient exchange. Random forest and SVM/KNN models confirmed these configurations as optimal, while PCA highlighted distinct patterns supporting medium pore size and porosity for enhanced osteogenesis.
Conclusions: This study integrates experiments with machine learning to optimize 3D biomaterial scaffold design for bone regeneration. Using models like regression, random forests, SVM, KNN, and PCA, scaffolds with a pore size of 150–200 μm and porosity of 50–70% were identified as optimal for osteoblast proliferation and differentiation. This data-driven approach enhances scaffold optimization and accelerates advanced tissue engineering development.
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